Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Jongwook Woo"'
Autor:
Jongsun Park, Yongsung Kim, Hong Xi, Kyoung-In Heo, Juhyeon Min, Jongwook Woo, Dukgou Lee, Youmi Seo, Yong Han Kim
Publikováno v:
Mitochondrial DNA. Part B. Resources, Vol 5, Iss 2, Pp 1619-1621 (2020)
We presented two complete chloroplast genomes of cold resistance coffee trees (Coffea arabica L.), named as CH1 and CH2 cultivated in Jeju island, Korea. Their length is 155,187 bp long (GC ratio is 37.4%) and has four subregions: 85,160 bp of large
Externí odkaz:
https://doaj.org/article/397948515e16446cb8581781863491ec
Autor:
Jongsun Park, Hong Xi, Yongsung Kim, Kyeong-In Heo, Myeungcheol Nho, Jongwook Woo, Youmi Seo, Ji Hyun Yang
Publikováno v:
Mitochondrial DNA. Part B. Resources, Vol 4, Iss 1, Pp 1083-1084 (2019)
Coffea arabica is major cultivated species of coffee. We selected cold hardiness of C. arabica (named as CH3) based on selection of coffee seeds in Jeju Island, Korea. Here, we presented complete chloroplast genome of cold resistance C. arabica which
Externí odkaz:
https://doaj.org/article/686c132342f04b6ea8a31e5f2d20a84e
Publikováno v:
Mitochondrial DNA. Part B. Resources, Vol 4, Iss 1, Pp 1541-1542 (2019)
C iComplete chloroplast genome of Coffea arabica of high production individual (HP1) is 155,191 bp long and has four subregions: 85,164 bp of large single copy (LSC) and 18,135 bp of small single copy (SSC) regions, separated by 25,946 bp of inverted
Externí odkaz:
https://doaj.org/article/f4145523e5b9405b84d7f263f62bb642
Autor:
Jongwook Woo
Publikováno v:
Issues in Information Systems, Vol 8, Iss 2, Pp 103-106 (2007)
Externí odkaz:
https://doaj.org/article/a1217e6f0e514ccb95346f7623350d14
Publikováno v:
2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd19c195f14e7de9fa2d1c372c3b82c0
https://doi.org/10.1109/icce-asia57006.2022.9954633
https://doi.org/10.1109/icce-asia57006.2022.9954633
Publikováno v:
Asia Pacific Journal of Information Systems. 31:185-196
The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1bcd28ac16b953f6d93332bb0cf41a4f
https://doi.org/10.14329/apjis.2021.31.2.185
https://doi.org/10.14329/apjis.2021.31.2.185
Autor:
Yong Han Kim, Jongsun Park, Jongwook Woo, Kyoung-In Heo, Yongsung Kim, Hong Xi, Dukgou Lee, Youmi Seo, Juhyeon Min
Publikováno v:
Mitochondrial DNA Part B. 5:1619-1621
We presented two complete chloroplast genomes of cold resistance coffee trees (Coffea arabica L.), named as CH1 and CH2 cultivated in Jeju island, Korea. Their length is 155,187 bp long (GC ratio i...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35b464de98d81b41ae47809b7fba83ff
https://doi.org/10.1080/23802359.2020.1715883
https://doi.org/10.1080/23802359.2020.1715883
Autor:
Jongwook Woo, Ruchi Singh
Publikováno v:
Asia Pacific Journal of Information Systems. 29:35-49
The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b7a06764de8d1865663f975353ac27f
https://doi.org/10.14329/apjis.2019.29.1.35
https://doi.org/10.14329/apjis.2019.29.1.35
Autor:
Jongwook Woo, Monika Mishra
Publikováno v:
WIREs Data Mining and Knowledge Discovery. 11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3d5a4f151a38a6c68112852f80668c0
https://doi.org/10.1002/widm.1400
https://doi.org/10.1002/widm.1400
Publikováno v:
Asia Pacific Journal of Information Systems. 28:308-319
This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ffb482b2973c759dff70de448746800a
https://doi.org/10.14329/apjis.2018.28.4.308
https://doi.org/10.14329/apjis.2018.28.4.308